DM2F-Net

By Zijun Deng, Lei Zhu, Xiaowei Hu, Chi-Wing Fu, Xuemiao Xu, Qing Zhang, Jing Qin, and Pheng-Ann Heng.

This repo is the implementation of "Deep Multi-Model Fusion for Single-Image Dehazing" (ICCV 2019), written by Zijun Deng at the South China University of Technology.

Results

The dehazing results can be found at Google Drive.

Installation & Preparation

Make sure you have Python>=3.7 installed on your machine.

Environment setup:

1. Create conda environment

   conda create -n dm2f
   conda activate dm2f
   

2. Install dependencies (test with PyTorch 1.8.0):

   1. Install pytorch==1.8.0 torchvision==0.9.0 (via conda, recommend).

   2. Install other dependencies

      pip install -r requirements.txt
      

• Prepare the dataset

  • Download the RESIDE dataset from the official webpage.

  • Download the O-Haze dataset from the official webpage.

  • Make a directory ./data and create a symbolic link for uncompressed data, e.g., ./data/RESIDE.

Training

1. ~~Set the path of pretrained ResNeXt model in resnext/config.py~~
2. Set the path of datasets in tools/config.py
3. Run by python train.py

~~The pretrained ResNeXt model is ported from the official torch version, using the convertor provided by clcarwin. You can directly download the pretrained model ported by me.~~

Use pretrained ResNeXt (resnext101_32x8d) from torchvision.

Hyper-parameters of training were set at the top of train.py, and you can conveniently change them as you need.

Training a model on a single ~~GTX 1080Ti~~ TITAN RTX GPU takes about ~~4~~ 5 hours.

Testing

1. Set the path of five benchmark datasets in tools/config.py.
2. Put the trained model in ./ckpt/.
3. Run by python test.py

Settings of testing were set at the top of test.py, and you can conveniently change them as you need.

License

DM2F-Net is released under the MIT license.

Citation

If you find the paper or the code helpful to your research, please cite the project.

@inproceedings{deng2019deep,
  title={Deep multi-model fusion for single-image dehazing},
  author={Deng, Zijun and Zhu, Lei and Hu, Xiaowei and Fu, Chi-Wing and Xu, Xuemiao and Zhang, Qing and Qin, Jing and Heng, Pheng-Ann},
  booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},
  pages={2453--2462},
  year={2019}
}